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There are at least two different kinds of heat maps:

1. Heatmaps representing concentration of points, and
2. Heatmaps representing distributions of attribute values

Every method has advantages and problems, I'm afraid going into detail is far beyond this Q&A.

I'll try to list some methods and functions for QGIS and GRASS.

Concentration of points

If you are tracking movement of wildlife, vehicles, etc. it can be useful to assess regions with high concentration of location messages.

Tools: e.g. QGIS Heatmap plugin (available in versions > 1.7.x) or GRASS v.neighbors or v.kernel

Distributions of attribute values

Here, we're basically talking more or less about interpolation methods. Methods include:

1. IDW

Depending on the implementation this can be global (using all available points in the set) or local (limited by number of points or maximum distance between points and interpolated position).

Tools: QGIS interpolation plugin (global), GRASS v.surf.idw or r.surf.idw (local)

2. Splines

Again, huge number of possible implementations. B-Splines are popular.

Tools: GRASS v.surf.bspline

3. Kriging

Statistical method with various sub-types.

Tools: GRASS v.krige (thanks to om_henners for the tip) or using R.

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There are at least two different kinds of heat maps:

1. Heatmaps representing concentration of points, and
2. Heatmaps representing distributions of attribute values

Every method has advantages and problems, I'm afraid going into detail is far beyond this Q&A.

I'll try to list some methods and functions for QGIS and GRASS.

Concentration of points

If you are tracking movement of wildlife, vehicles, etc. it can be useful to assess regions with high concentration of location messages.

Tools: e.g. QGIS Heatmap plugin (available in versions > 1.7.x) or GRASS v.neighbors or v.kernel

Distributions of attribute values

Here, we're basically talking more or less about interpolation methods. Methods include:

1. IDW

Depending on the implementation this can be global (using all available points in the set) or local (limited by number of points or maximum distance between points and interpolated position).

Tools: QGIS interpolation plugin (global), GRASS v.surf.idw or r.surf.idw (local)

2. Splines

Again, huge number of possible implementations. B-Splines are popular.

Tools: GRASS v.surf.bspline

3. Kriging

Statistical method with various sub-types.

Tools: GRASS v.krige (thanks to om_henners for the tip) or using R.

There are at least two different kinds of heat maps:

1. Heatmaps representing concentration of points, and
2. Heatmaps representing distributions of attribute values

Every method has advantages and problems, I'm afraid going into detail is far beyond this Q&A.

I'll try to list some methods and functions for QGIS and GRASS.

Concentration of points

If you are tracking movement of wildlife, vehicles, etc. it can be useful to assess regions with high concentration of location messages.

Tools: e.g. QGIS Heatmap plugin (available in versions > 1.7.x) or GRASS v.neighbors or v.kernel

Distributions of attribute values

Here, we're basically talking more or less about interpolation methods. Methods include:

1. IDW

Depending on the implementation this can be global (using all available points in the set) or local (limited by number of points or maximum distance between points and interpolated position).

Tools: QGIS interpolation plugin (global), GRASS v.surf.idw or r.surf.idw (local)

2. Splines

Again, huge number of possible implementations. B-Splines are popular.

Tools: GRASS v.surf.bspline

3. Kriging

Statistical method with various sub-types.

Tools: GRASS v.krige (thanks to om_henners for the tip) or using R.

There are at least two different kinds of heat maps:

1. Heatmaps representing concentration of points, and
2. Heatmaps representing distributions of attribute values

Every method has advantages and problems, I'm afraid going into detail is far beyond this Q&A.

I'll try to list some methods and functions for QGIS and GRASS.

Concentration of points

If you are tracking movement of wildlife, vehicles, etc. it can be useful to assess regions with high concentration of location messages.

Tools: e.g. QGIS Heatmap plugin (available in versions > 1.7.x) or GRASS v.neighbors or v.kernel

Distributions of attribute values

Here, we're basically talking more or less about interpolation methods. Methods include:

1. IDW

Depending on the implementation this can be global (using all available points in the set) or local (limited by number of points or maximum distance between points and interpolated position).

Tools: QGIS interpolation plugin (global), GRASS v.surf.idw or r.surf.idw (local)

2. Splines

Again, huge number of possible implementations. B-Splines are popular.

Tools: GRASS v.surf.bspline

3. Kriging

Statistical method with various sub-types.

Tools: GRASS v.krige (thanks to om_henners for the tip) or using R.

There are at least two different kinds of heat maps:

1. Heatmaps representing concentration of points, and
2. Heatmaps representing distributions of attribute values

Every method has advantages and problems, I'm afraid going into detail is far beyond this Q&A.

I'll try to list some methods and functions for QGIS and GRASS.

Concentration of points

If you are tracking movement of wildlife, vehicles, etc. it can be useful to assess regions with high concentration of location messages.

Tools: e.g. GRASS v.neighbors or v.kernel

Distributions of attribute values

Here, we're basically talking more or less about interpolation methods. Methods include:

1. IDW

Depending on the implementation this can be global (using all available points in the set) or local (limited by number of points or maximum distance between points and interpolated position).

Tools: QGIS interpolation plugin (global), GRASS v.surf.idw or r.surf.idw (local)

2. Splines

Again, huge number of possible implementations. B-Splines are popular.

Tools: GRASS v.surf.bspline

3. Kriging

Statistical method with various sub-types.

Tools: GRASS v.krige (thanks to om_henners for the tip) or using R.

There are at least two different kinds of heat maps:

1. Heatmaps representing concentration of points, and
2. Heatmaps representing distributions of attribute values

Every method has advantages and problems, I'm afraid going into detail is far beyond this Q&A.

I'll try to list some methods and functions for QGIS and GRASS.

Concentration of points

If you are tracking movement of wildlife, vehicles, etc. it can be useful to assess regions with high concentration of location messages.

Tools: e.g. QGIS Heatmap plugin (available in versions > 1.7.x) or GRASS v.neighbors or v.kernel

Distributions of attribute values

Here, we're basically talking more or less about interpolation methods. Methods include:

1. IDW

Depending on the implementation this can be global (using all available points in the set) or local (limited by number of points or maximum distance between points and interpolated position).

Tools: QGIS interpolation plugin (global), GRASS v.surf.idw or r.surf.idw (local)

2. Splines

Again, huge number of possible implementations. B-Splines are popular.

Tools: GRASS v.surf.bspline

3. Kriging

Statistical method with various sub-types.

Tools: GRASS v.krige (thanks to om_henners for the tip) or using R.